{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d8d0947",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d30f17e",
   "metadata": {},
   "source": [
    "# 常见图形绘制\n",
    "## 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32eb2b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#0数据准备\n",
    "x=[i for i in range(1,10)]\n",
    "y=[random.uniform(10,15) for i in x]\n",
    "\n",
    "# 1创建画布\n",
    "plt.figure(figsize=(10,8),dpi=80)\n",
    "\n",
    "\n",
    "# 2绘制图像\n",
    "plt.plot(x,y,label=\"shuju\")\n",
    "\n",
    "# 3添加辅助层\n",
    "# x,y刻度显示内容+刻度单位构造\n",
    "x_ticks_label=[\"shu{}\".format(i) for i in x]#构建刻度标\n",
    "y_ticks_label=range(10,15)\n",
    "\n",
    "plt.xticks(x[::2],x_ticks_label[::2])\n",
    "plt.yticks(y[::1])\n",
    "\n",
    "# X,Y轴描述\n",
    "plt.xlabel=(\"x_data\")\n",
    "plt.ylabel=(\"y_data\")\n",
    "\n",
    "plt.grid(True,linestyle=\"--\")\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"SHUJUCESHI\",fontsize=20,color=\"r\")\n",
    "\n",
    "\n",
    "# 4展示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b806a8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7f79109d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pn87o0aN54YUXKus9+OCDnDx5kv379wPQ2NjIQw89xPbt2zl58iQATU1NTJ8+ndWrV1fWmz9/Pr29vbz88st9uWDlypXs3bu3cvP4mDFjuPfee9mwYUNlhOKMGTMYOXIkmzdvrmzr4Ycf5sSJExw4cACAIUOG8MADD9DS0kJHRwdQmux46tSprFmzprJe3z09fZMg19XVsWLFCvbs2UPf7Shjx45lyZIlrF+/vvIs7lmzZjFs2LC3zXu4dOlSjh8/ziuvvAKUcr1kyRK2bt1amXB5ypQpTJkyhXXr1lXWW7hwIRcuXGDXrl1AaWTm8uXL2b17d+U+x3HjxjF37lzWrVtXuQdp9uzZNDY2Vm6OB3j00Udpb2+vTF1Si+OpoaGBbdu2Vbbl8VSycuVK9u3b5/F0leOpubnZ42n8eNoujuAfzelmUCjl6BuHB9A0JPLk3T2EAK/u3kLzD/yQxxO37ufTmDFjePPNN7n99ts5e/ZsZb3GxtJtF33b7vv3f/PNN3nrrbeAUrZuv/12zp07R29vb+VrHDhwYOXvBUrZ7e7urhxzdXV1DBkyhDfeeKPyvXTw4MEMHjz4bTXcfvvtxBgrNYQQGDZs2Ntq6Ju+6vIabrvtNurq6vpVw/nz5yv/9oMHD2bQoEGcOXOGN998kzVr1rzjeLqeft2jWvlwCG3AR2OMrSGED1JqIh+OMXaHENYDv3a1e1Sv2MYg4NeBQzHGX73eshDC3waaY4w/V14eyfE9qlu3bq3MRyb1h5lRKjOjG2lp6+Dxp7fQ3VP6+f7kPT38zndKZ8jq6wJf+8xiz6jeYgcPHmTo0KHccccdubt83x9vvPEGt99+c1NiXS7GyKlTpzh37hxTp059x/Lr3aN6M2dUHwImA6+U/xHGAV8JIdwZY/yt6xT7Vgjhf1Bqcn/1BsseBR4OIXzoso/tCiH89RjjyzdR+y3R979xqb/MjFKZGd1I31RHR0510dMbabq91LBWa6oj3VhTUxPt7e2VKwVF8+abb3LbbbdVdZv19fU0NTUlr/euG9VyM1ppSK93RjWEMAl4PcbYVb6v9W9SusR/3WUxxp+8YjsRmNPfM6qSJL3f9E111DfqP4TSmdSmUdWZ6qjoajGyftCgQUydOpXe3t5CDl5bs2YNK1asqNr2QgjvevBVvxrVEMJuYAalKaTWhhA6Y4xTL1v+08BS4EXgW+X3fhk4HmP878CHgX8fQujb37Hy5ykv+4+XhaSH8gj/EMIdwNoryjkZQvhAjDF3/02ZMmVK1iWoYMyMUpkZ9ceEkY2sfWopLx3ppP3wIb72mbsym+ooTy6ftqu+bgDdPb23dNquWo2Mr7YpU6YkT611q/R3HtWHge8CL1C+R/WyZZOBrwEB+A/XOKM6DbgtxrirPLL/a8C+GOMXrvLZ3wBijPFzV1n2eWBpjPHD/fnian2Pand3d+UGZKk/zIxSmRmlMjMlMUaWf3FD5ZaIPnUDShPrr3lq6fu+ke9T68zc9DyqMcaNMcZ3dHxXTE918R0rfn/9V2KMfZfze4AWSve3Xrm9BuAngd+5xqaevM6yzF0+QlTqDzOjVGZGqcxMyZXTdvW5fNouleQpMzWbnqpPCOF2SvOh/rOrLP4Y8N0rn3xVXm8JMBL4k+tsO9PpqXp7e52eyumpgP5PpwM4nY7TUwH9P55ijE5P5fRUQP+Pp56eHjo7O9/3x9O52ycwbXjkxyZfqmzrCzvq+OGJvdz/gciru7cw4NRYjyeo5LdWx9P11HR6qvL0U/+HUjP681dZvhb4wxjjf7vKst8BTsUY/3F/6/URqso7M6NUZkapzEzJldN2Xc5pu96u1pnJxfRUIYR64A+AvwJ+4SrLpwCLgY9fZdkQ4MeABTdR7y23cOHCrEtQwZgZpTIzSmVmSq6ctquP03a9U54y866Ho8UYfyvGeGeMcXKMcTKwBfg712hSBwJfBzrKn7naadxPA//3GlNPfQLYGWPc/27rrYW+U/RSf5kZpTIzSmVmSvqm7Zp0RyP1dYHGQXXU15UGUjlt19vlKTP9nZ7qy8CPUDpruiqEcO7y6amusc7l01N9gtL9p7uAl8th2BRj/Hvlzw4Afgp44hqbe5LSbQa5tmvXLu68886sy1CBmJm3q8X8hkVnZpTKzHzf5dN2+X3m2vKUmf5e+r8IXKI0j+pjVw52umwe1V/rey/G+C8u+8gu4HngA+XtbAM+f9ny/13e9roQQuURqeWBV+uABuDvhxA+AfxsjLGtn3VLKohaz28o6f0phMCCyaO8H7Ug+nvp/5vAg8CRKxeU51H9DKVL/9dyAfi5GON0YC5wO/BPLlv+34Hmq6z3JrAixjg3xjgXWAX8137WXHMDB97sJAp6vzEzJTFGnnhmG0dOddHdE+l6q4funsiRU1186plthXyyy61iZpTKzChVnjKTi3lUY4xrYozfu8p6vTHGc+V9BWAYkNufWMuXL8+6BBWMmSlxfsP+MzNKZWaUKk+Zyds8qtdaZw0wG3gduOZ8CVnPo3rs2LHK3GXw/pynznlUS/o772NdXR2nTp1638/72HZxBP9oTjeDQilH3zg8gLPdgSfv7iEEeHX3Fpp/4Ic8noA777yT+vp6jyfnUe33z6fhw4dzzz33vO/nUfXnU0l/jqe+bDmP6juXR6Byj+oVywYAvwjcGWP8bH/qdR5V5Z2ZKXF+w/4zM0plZpQqT/OovuvpqXj7PKptlOZA/UoI4e9eo4jrzqN6IzHGXkqN8SffZb2ScqpvfsO6AW+/MuP8hpL0/paneVTfIYQwLoRw+U+oT1CaQSCXxo0bl3UJKhgzU+L8hv1nZpTKzChVnjLTr0v/V8yjegp4xzyqV176v3we1RDCTwK/T6nJ7Nvh5fOo/iml2QB+ADgOvBJjfCSEsBD4MqWpqwJwCPgHMcbD/fnian3pX9LNcR5VSXr/ud6l/6R7VIum1o3qunXrWLZsWc32p+IzM0plZpTKzChVrTNzq+5R1RX6RnlK/WVmlMrMKJWZUao8ZcZGVZIkSblko1pFs2fPzroEFYyZUSozo1RmRqnylBkb1SpqbPR55EpjZpTKzCiVmVGqPGXGRrWKLn/yiNQfZkapzIxSmRmlylNmbFQlSZKUSzaqkiRJyiXnUa2it956i0GDBtVsfyo+M6NUZkapzIxS1TozzqNaI+3t7VmXoIIxM0plZpTKzChVnjJjo1pFhw4dyroEFYyZUSozo1RmRqnylBkbVUmSJOWSjWoVDR8+POsSVDBmRqnMjFKZGaXKU2YcTCVJkqTMOJiqRrZs2ZJ1CSoYM6NUZkapzIxS5SkzNqpVdObMmaxLUMGYGaUyM0plZpQqT5mxUZUkSVIu2ahW0V133ZV1CSoYM6NUZkapzIxS5SkzDqaqIp/+oVRmRqnMjFKZGaXyyVTvUc8991zWJahgzIxSmRmlMjNKlafM2KhKkiQpl2xUJUmSlEveo1pFp0+fZsSIETXbn4rPzCiVmVEqM6NUtc6M96jWSFdXV9YlqGDMjFKZGaUyM0qVp8zYqFbR7t27sy5BBWNmlMrMKJWZUao8ZcZGVZIkSblko1pF9fX1WZeggjEzSmVmlMrMKFWeMuNgKkmSJGXGwVQ1snPnzqxLUMGYGaUyM0plZpQqT5mxUa2iEydOZF2CCsbMKJWZUSozo1R5yoyNqiRJknLJRrWKxo8fn3UJKhgzo1RmRqnMjFLlKTMOppIkSVJmHExVI2vXrs26BBWMmVEqM6NUZkap8pQZG9UqunTpUtYlqGDMjFKZGaUyM0qVp8zYqEqSJCmXbFSraM6cOVmXoIIxM0plZpTKzChVnjJjo1pFDQ0NWZeggjEzSmVmlMrMKFWeMmOjWkXbtm3LugQVjJlRKjOjVGZGqfKUGRtVSZIk5ZKNqiRJknLJCf+rqLu7m/r6+prtT8VnZpTKzCiVmVGqWmfGCf9r5PDhw1mXoIIxM0plZpTKzChVnjJjo1pFefqHVTGYGaUyM0plZpQqT5mxUZUkSVIu2ahW0YgRI7IuQQVjZpTKzCiVmVGqPGXGwVSSJEnKjIOpamTz5s1Zl6CCMTNKZWaUyswoVZ4yY6NaRefOncu6BBWMmVEqM6NUZkap8pQZG1VJkiTlko1qFU2bNi3rElQwZkapzIxSmRmlylNmbFSraPz48VmXoIIxM0plZpTKzChVnjJjo1pFGzZsyLoEFYyZUSozo1RmRqnylBkbVUmSJOWSjaokSZJyyQn/q+js2bMMGzasZvtT8ZkZpTIzSmVmlKrWmXHC/xo5e/Zs1iWoYMyMUpkZpTIzSpWnzNioVtGePXuyLkEFY2aUyswolZlRqjxlxkZVkiRJuWSjWkWDBw/OugQVjJlRKjOjVGZGqfKUGQdTSZIkKTMOpqqR1tbWrEtQwZgZpTIzSmVmlCpPmbFRraLXXnst6xJUMGZGqcyMUpkZpcpTZmxUJUmSlEs2qlU0YcJVb6+QrsnMKJWZUSozo1R5yoyDqSRJkpQZB1PVyJo1a7IuQQVjZpTKzCiVmVGqPGXGRrWKenp6si5BBWNmlMrMKJWZUao8ZcZGVZIkSblko1pFzc3NWZeggjEzSmVmlMrMKFWeMmOjWkX19fVZl6CCMTNKZWaUyswoVZ4yY6NaRS0tLVmXoIIxM0plZpTKzChVnjJjoypJkqRcslGVJElSLjnhfxX19PRQV1dXs/2p+MyMUpkZpTIzSlXrzDjhf40cPHgw6xJUMGZGqcyMUpkZpcpTZmxUq6itrS3rElQwZkapzIxSmRmlylNmbFQlSZKUSzaqVTRq1KisS1DBmBmlMjNKZWaUKk+ZcTCVJEmSMuNgqhrZtGlT1iWoYMyMUpkZpTIzSpWnzNioVtH58+ezLkEFY2aUyswolZlRqjxlxkZVkiRJuWSjWkV333131iWoYMyMUpkZpTIzSpWnzNioVtG4ceOyLkEFY2aUyswolZlRqjxlxka1ijZu3Jh1CSoYM6NUZkapzIxS5SkzNqqSJEnKJRtVSZIk5ZIT/lfRuXPnGDp0aM32p+IzM0plZpTKzChVrTPjhP810tnZmXUJKhgzo1RmRqnMjFLlKTM2qlW0b9++rEtQwZgZpTIzSmVmlCpPmbFRlSRJUi7ZqFZRQ0ND1iWoYMyMUpkZpTIzSpWnzDiYSpIkSZlxMFWN7NixI+sSVDBmRqnMjFKZGaXKU2ZsVKvo9ddfz7oEFYyZUSozo1RmRqnylBkbVUmSJOWSjWoVTZw4MesSVDBmRqnMjFKZGaXKU2YcTCVJkqTMOJiqRlavXp11CSoYM6NUZkapzIxS5SkzNqpV9F4+O61bw8wolZlRKjOjVHnKjI2qJEmScslGtYrmzZuXdQkqGDOjVGZGqcyMUuUpMzaqVTRggH+dSmNmlMrMKJWZUao8ZSY/lbwHbN++PesSVDBmRqnMjFKZGaXKU2ZsVCVJkpRLNqqSJEnKJSf8r6IYIyGEmu1PxWdmlMrMKJWZUapaZ8YJ/2tk//79WZeggjEzSmVmlMrMKFWeMmOjWkXt7e1Zl6CCMTNKZWaUyswoVZ4yY6MqSZKkXLJRraLRo0dnXYIKxswolZlRKjOjVHnKjIOpJEmSlBkHU9XI888/n3UJKhgzo1RmRqnMjFLlKTM2qlXU1dWVdQkqGDOjVGZGqcyMUuUpMzaqkiRJyiUb1SqaPn161iWoYMyMUpkZpTIzSpWnzNioVlGeRsmpGMyMUpkZpTIzSpWnzNioVtELL7yQdQkqGDOjVGZGqcyMUuUpMzaqkiRJyiUbVUmSJOWSE/5X0RtvvMHtt99es/2p+MyMUpkZpTIzSlXrzDjhf42cPHky6xJUMGZGqcyMUpkZpcpTZmxUq2j//v1Zl6CCMTNKZWaUyswoVZ4yY6MqSZKkXLJRraLGxsasS1DBmBmlMjNKZWaUKk+ZcTCVJEmSMuNgqhrZvn171iWoYMyMUpkZpTIzSpWnzNioVlGeRsmpGMyMUpkZpTIzSpWnzNioSpIkKZdsVKuoqakp6xJUMGZGqcyMUpkZpcpTZhxMVUUxRkIINdufis/MKJWZUSozo1S1zoyDqWpk9erVWZeggjEzSmVmlMrMKFWeMmOjKkmSpFyyUZUkSVIu2ahW0fz587MuQQVjZpTKzCiVmVGqPGXGRrWKent7sy5BBWNmlMrMKJWZUao8ZcZGtYpefvnlrEtQwZgZpTIzSmVmlCpPmbFRlSRJUi7ZqFaR89QplZlRKjOjVGZGqfKUGSf8lyRJUmac8L9G9u7dm3UJKhgzo1RmRqnMjFLlKTM2qlV09OjRrEtQwZgZpTIzSmVmlCpPmbFRlSRJUi7ZqFbRmDFjsi5BBWNmlMrMKJWZUao8ZcbBVJIkScqMg6lqZMOGDVmXoIIxM0plZpTKzChVnjJjo1pFFy5cyLoEFYyZUSozo1RmRqnylBkbVUmSJOWSjWoVzZgxI+sSVDBmRqnMjFKZGaXKU2ZsVKto5MiRWZeggjEzSmVmlMrMKFWeMmOjWkWbN2/OugQVjJlRKjOjVGZGqfKUGRtVSZIk5ZKNqiRJknLJCf+r6M033+S2226r2f5UfGZGqcyMUpkZpap1Zpzwv0ZOnDiRdQkqGDOjVGZGqcyMUuUpMzaqVXTgwIGsS1DBmBmlMjNKZWaUKk+ZsVGVJElSLtmoVtGQIUOyLkEFY2aUyswolZlRqjxlxsFUkiRJyoyDqWqkpaUl6xJUMGZGqcyMUpkZpcpTZmxUq6ijoyPrElQwZkapzIxSmRmlylNmbFQlSZKUSzaqVTR58uSsS1DBmBmlMjNKZWaUKk+ZcTBVFfX09FBXV1ez/an4zIxSmRmlMjNKVevMOJiqRtasWZN1CSoYM6NUZkapzIxS5SkzNqqSJEnKJRtVSZIk5ZL3qFZRR0cHo0aNqtn+VHxmRqnMjFKZGaWqdWa8R7VGuru7sy5BBWNmlMrMKJWZUao8ZcZGtYpaW1uzLkEFY2aUyswolZlRqjxlxkZVkiRJuWSjWkXOU6dUZkapzIxSmRmlylNmHEwlSZKkzDiY6haLMdLS1sH/WfsiLW0dvJebf1XXnj17si5BBWNmlMrMKFWeMtOvRjWE8KUQQlsIIYYQmsvvNYQQvhVCOBBC2BlC+IsQwtRrrP9YCKH1sl/HQwg7ystuDyFsLW9jZwjhz0MIky9bd1H5/QMhhHUhhB+4+S+7eo51drH8ixt4/OktnO/4Ho8/vYXlX9zAsc6urEtTAXjGX6nMjFKZGaXKU2b6e0b1m8CDwJEr3v8KcE+McS7wR8BvX23lGOOqGGNz3y9gB/A/y4vfBFbEGOeWt7MK+K8AIYQB5c/9/Rjj3cCfAb/Wz5pvuRgjTzyzjSOnuujuicQI3T2RI6e6+NQz2zyzKkmSdBP61ajGGDfGGI9d8d6FGOOfxe93Y1uAyTfaVghhPLAc+L3ydnpjjOfKywIwDOjb5nzgUozxufLrLwMfDiE09KfuW+2lI50c63iTnt5SuXtPBwB6eiPtHV28dKQzy/JUAGPHjs26BBWMmVEqM6NUecrMwCpu6xconVW9kZ8C/izG+L3L3wwhrAFmA68Dj5XfbuKys7gxxnMhhLPAeOC7V244hPAU8FTf66FDh7Jq1SoAhg8fzuLFi9myZQtnzpwB4K677qKpqYnnnnuuso1FixbR1dXF7t27Aaivr2fZsmXs3LmTEydOADB+/Hhmz57Nq3u28UvNbxEjfOPwAF783gD+1b2XyrVA2/fOMvytkxw+fBiAESNGsGjRIjZv3sy5c+cAmDZtGuPHj2fDhg2VGu6//37Onj1buUdk8ODBPPLII7S2tvLaa68BMGHCBGbNmsWaNWvo6ekBoLm5mfr6elpaWirbWrFiBQcPHqStrQ2AUaNGsWDBAjZt2sT58+cBuPvuuxk3bhwbN26srLdkyRI6OzvZt28fAA0NDSxdupQdO3bw+uuvAzBx4kRmzpzJ6tWrK2eP582bx4ABA9i+fXtlWytXrmT//v20t7cDMHr0aObPn8/zzz9PV1fpFonp06czevRoXnjhhcp6Dz74ICdPnmT//v0ANDY28tBDD7F9+3ZOnjwJQFNTE9OnT2f16tWV9ebPn09vby8vv/xyXy5YuXIle/fu5ejRowCMGTOGe++9lw0bNnDhwgUAZsyYwciRI9m8eXNlWw8//DAnTpzgwIEDAAwZMoQHHniAlpYWOjo6AJg8eTJTp05lzZo1lfUWLFhAd3d3ZS66uro6VqxYwZ49eyqXVMaOHUtzczPr16/n4sWLAMyaNYthw4bx4osvVra1dOlSjh8/ziuvvAKUcr1kyRK2bt3K6dOnAZgyZQpTpkxh3bp1lfUWLlzIhQsX2LVrFwADBw5k+fLl7N69m+PHjwMwbtw45s6dy7p16yoTPM+ePZvGxka2bt1a2dajjz5Ke3s7hw4dAm7N8bR27VouXSodP3PmzKGhoYFt27ZVtrVs2TIOHz7s8bRyJfv27fN48nhKOp46Ozs9nvz5BPT/eAJqdjxdT9Ko/xBCG/DRGGPrFe//c+DDwPIY4zVvziyfMX0F+PkY459dZfkA4BeBO2OMnw0hfBz4OzHGxy77zPeAxTHGdzSqV7rVo/5b2jp4/OktdPeU/g4/P/sS/2l3qfevrwt87TOLWTDZx9bp2tavX88jjzySdRkqEDOjVGZGqWqdmVs66j+E8HngY8D/c70mtWwp0EDpPtR3iDH2Ak8Dnyy/1Q5MumxfQ4HhwPGbLLsq7ps0komjGqkbULrkP7S+9H7dgEDTqEbumzQyw+pUBH3/U5X6y8wolZlRqjxl5qYa1fKl9p8AfijGeLofqzwJfDXG2HPZNsaFEC7v6D4B9J0L3g7UhxAeLb/+GeCPY4wXbqbuagkh8OynFzLpjkbq6wIhlM6kTr6jkWefXETpBLIkSZLejX5d+g8hfBn4EWAccAo4BzwCHKV0r+i58kcvxhgXldf5ZeB4jPG/l1/3nQmdffll+xDCQkqDpOqAABwC/kGM8XB5+f3l5Q3l9T8ZYzzany+uVhP+xxh56Ugn7e1HaWqayH2TRtqkql+OHTvGhAlXvdohXZWZUSozo1S1zsz1Lv37ZKoqOnv2LMOGDavZ/lR8ZkapzIxSmRmlqnVmfDJVjVw+Ek7qDzOjVGZGqcyMUuUpMzaqkiRJyiUbVUmSJOWS96hW0YULF2hoyMVDs1QQZkapzIxSmRmlqnVmvEe1RvqeTiL1l5lRKjOjVGZGqfKUGRvVKup7hJjUX2ZGqcyMUpkZpcpTZmxUJUmSlEs2qlU0dOjQrEtQwZgZpTIzSmVmlCpPmXEwlSRJkjLjYKoa2bp1a9YlqGDMjFKZGaUyM0qVp8zYqFbR6dOnsy5BBWNmlMrMKJWZUao8ZcZGVZIkSblko1pFU6ZMyboEFYyZUSozo1RmRqnylBkHU1VRd3c39fX1Ndufis/MKJWZUSozo1S1zoyDqWpk3bp1WZeggjEzSmVmlMrMKFWeMmOjKkmSpFyyUZUkSVIueY9qFXV2djJy5Mia7U/FZ2aUyswolZlRqlpnxntUa+TChQtZl6CCMTNKZWaUyswoVZ4yY6NaRbt27cq6BBWMmVEqM6NUZkap8pQZG1VJkiTlko1qFQ0cODDrElQwZkapzIxSmRmlylNmHEwlSZKkzDiYqkZ2796ddQkqGDOjVGZGqcyMUuUpMzaqVXT8+PGsS1DBmBmlMjNKZWaUKk+ZsVGVJElSLtmoVtG4ceOyLkEFY2aUyswolZlRqjxlxsFUkiRJyoyDqWpk3bp1WZeggjEzSmVmlMrMKFWeMmOjWkXd3d1Zl6CCMTNKZWaUyswoVZ4yY6MqSZKkXLJRraLZs2dnXYIKxswolZlRKjOjVHnKjI1qFTU2NmZdggrGzCiVmVEqM6NUecqMjWoVbd26NesSVDBmRqnMjFKZGaXKU2ZsVCVJkpRLNqqSJEnKJSf8r6K33nqLQYMG1Wx/Kj4zo1RmRqnMjFLVOjNVmfA/hPClEEJbCCGGEJrL7zWEEL4VQjgQQtgZQviLEMLUa6w/JYSwPYTQGkL4yxDCN0III8vLJocQesrL+n7ddaP18qa9vT3rElQQMUZa2jr44027aGnr4L38H0ZVl99nlMrMKFWeMpNy6f+bwIPAkSve/wpwT4xxLvBHwG9fY/3jwIMxxuYY4wfLr79w2fJz5WV9vw71c73cOHTo0I0/pPe9Y51dLP/iBh5/egtvnHyVx5/ewvIvbuBYZ1fWpakA/D6jVGZGqfKUmX43qjHGjTHGY1e8dyHG+Gfx+6eDtgCTr7H+xRjjmwAhhDrgduCGp5He7XpSHsUYeeKZbRw51UV3TyRG6O6JHDnVxaee2eaZVUmSLlPtwVS/QOms6lWFEAaFEFqBk8A04F9etvj2EEJLCGFHCOFflJvS/qyXG8OHD8+6BOXcS0c6OdbxJj29pYb01a4AQE9vpL2ji5eOdGZZngrA7zNKZWaUKk+ZGVitDYUQ/jkwFVh+rc/EGN8CmkMIg4BfB34G+FXgr4AfiDF+L4QwCvgD4B+Wl11vvStreAp4qu/10KFDWbVqFVD6S1+8eDFbtmzhzJkzANx11100NTXx3HPPVbaxaNEiurq62L17NwD19fUsW7aMnTt3cuLECQDGjx/P7NmzWbt2LZcuXQJgzpw53HPPPZX9ASxbtozDhw9z+PBhAEaMGMGiRYvYvHkz586dA2DatGmMHz+eDRs2VNa7//77OXv2LHv27AFg8ODBPPLII7S2tvLaa68BMGHCBGbNmsWaNWvo6ekBoLm5mfr6elpaWirbWrFiBQcPHqStrQ2AUaNGsWDBAjZt2sT58+cBuPvuuxk3bhwbN26srLdkyRI6OzvZt28fAA0NDSxdupQdO3bw+uuvAzBx4kRmzpzJ6tWrK2cC582bx4ABA9i+fXtlWytXrmT//v2Ve15Gjx7N/Pnzef755+nqKl3unj59OqNHj+aFF16orPfggw9y8uRJ9u/fD5QmIH7ooYfYvn07J0+eBKCpqYnp06ezevXqynrz58+nt7eXl19+uS8XrFy5kr1793L06FEAxowZw7333suGDRu4cOECADNmzGDkyJFs3ry5sq2HH36YEydOcODAAQCGDBnCAw88QEtLCx0dHQBMnjyZqVOnsmbNmsp6CxYsoLu7m9bWVgDq6upYsWIFRw99h19qfosYYe/pwFf21/H52ZcYWg8hQHv7Ue4ZNZAXX3yxsq2lS5dy/PhxXnnlFaCU6yVLlrB161ZOnz4NwJQpU5gyZQrr1q2rrLdw4UIuXLjArl27ABg4cCDLly9n9+7dHD9+HIBx48Yxd+5c1q1bV3m28+zZs2lsbHzbPHqPPvoo7e3tlctBtTieGhoa2LZtW2VbHk8lK1euZN++fR5PK1awZ88e+gbMjh07lubmZtavX8/FixcBmDVrFsOGDfN4mjOHzs5Ojyd/PgH9P56Amh1P15M86j+E0AZ8NMbYetl7nwd+HFgRYzzdz+0sBp6OMb7jOV0hhJ8AHo8xfjhlvSvVetT/li1bWLx4cc32p+Jpaevg8ae30N1TOu7+zvQevrK/dPGgvi7wtc8sZsHkUVmWqJzz+4xSmRmlqnVmqjLq/zobfwr4CeCHrtekhhAmhRAay38eAPxNYFf59QdCCPXlPw8GPga8fKP18qbvf8LStdw3aSQTRzVSN6B0yf8HGksNa92AQNOoRu6blMsJLZQjfp9RKjOjVHnKTMr0VF8OIRwDJgCrQggHQwgTgP8MjACeK08htfWydX45hPCz5ZdzgC0hhF2UGs0xwM+Xlz0IvBxC2AnsAE4A/6Yf60mFEkLg2U8vZNIdjdTXBUIonUmdfEcjzz65iBBC1iVKkpQbTvhfRQcPHmTq1KtOIyu9TYyRl4500n74uzRN+UHumzTSJlX94vcZpTIzSlXrzFzv0r+NahX59A+lMjNKZWaUyswoVSGfTKUbu3x0ptQfZkapzIxSmRmlylNmbFQlSZKUSzaqkiRJyiXvUa2i06dPM2LEiJrtT8VnZpTKzCiVmVGqWmfGe1RrpO8pFlJ/mRmlMjNKZWaUKk+ZsVGtor7H2kn9ZWaUyswolZlRqjxlxkZVkiRJuWSjWkX19fVZl6CCMTNKZWaUyswoVZ4y42AqSZIkZcbBVDWyc+fOrEtQwZgZpTIzSmVmlCpPmbFRraITJ05kXYIKxswolZlRKjOjVHnKjI2qJEmScslGtYrGjx+fdQkqGDOjVGZGqcyMUuUpMw6mkiRJUmYcTFUja9euzboEFYyZUSozo1RmRqnylBkb1Sq6dOlS1iWoYMyMUpkZpTIzSpWnzNioSpIkKZdsVKtozpw5WZeggjEzSmVmlMrMKFWeMmOjWkUNDQ1Zl6CCMTNKZWaUyswoVZ4yY6NaRdu2bcu6BBWMmVEqM6NUZkap8pQZG1VJkiTlko2qJEmScskJ/6uou7ub+vr6mu1PxWdmlMrMKJWZUapaZ8YJ/2vk8OHDWZeggjEzSmVmlMrMKFWeMmOjWkV5+odVMZgZpTIzSmVmlCpPmbFRlSRJUi7ZqFbRiBEjsi5BBWNmlMrMKJWZUao8ZcbBVJIkScqMg6lqZPPmzVmXoIIxM0plZpTKzChVnjJjo1pF586dy7oEFYyZUSozo1RmRqnylBkbVUmSJOWSjWoVTZs2LesSVDBmRqnMjFKZGaXKU2ZsVKto/PjxWZeggjEzSmVmlMrMKFWeMmOjWkUbNmzIugQVjJlRKjOjVGZGqfKUGRtVSZIk5ZKNqiRJknLJCf+r6OzZswwbNqxm+1PxmRmlMjNKZWaUqtaZccL/Gjl79mzWJahgzIxSmRmlMjNKlafM2KhW0Z49e7IuQQVjZpTKzCiVmVGqPGXGRlWSJEm5ZKNaRYMHD866BBWMmVEqM6NUZkap8pQZB1NJkiQpMw6mqpHW1tasS1DBmBmlMjNKZWaUKk+ZsVGtotdeey3rElQwZkapzIxSmRmlylNmbFQlSZKUSzaqVTRhwlVvr5CuycwolZlRKjOjVHnKjIOpJEmSlBkHU9XImjVrsi5BBWNmlMrMKJWZUao8ZcZGtYp6enqyLkEFY2aUyswolZlRqjxlxkZVkiRJuWSjWkXNzc1Zl6CCMTNKZWaUyswoVZ4yY6NaRfX19VmXoIIxM0plZpTKzChVnjJjo1pFLS0tWZeggjEzSmVmlMrMKFWeMmOjKkmSpFyyUZUkSVIuOeF/FfX09FBXV1ez/an4zIxSmRmlMjNKVevMOOF/jRw8eDDrElQwZkapzIxSmRmlylNmbFSrqK2tLesSVDBmRqnMjFKZGaXKU2ZsVCVJkpRLNqpVNGrUqKxLUMGYGaUyM0plZpQqT5lxMJUkSZIy42CqGtm0aVPWJahgzIxSmRmlMjNKlafM2KhW0fnz57MuQQVjZpTKzCiVmVGqPGXGRlWSJEm5ZKNaRXfffXfWJahgzIxSmRmlMjNKlafM2KhW0bhx47IuQQVjZpTKzCiVmVGqPGXGRrWKNm7cmHUJKhgzo1RmRqnMjFLlKTM2qpIkScolG1VJkiTlkhP+V9G5c+cYOnRozfan4jMzSmVmlMrMKFWtM+OE/zXS2dmZdQkqGDOjVGZGqcyMUuUpMzaqVbRv376sS1DBmBmlMjNKZWaUKk+ZsVGVJElSLtmoVlFDQ0PWJahgzIxSmRmlMjNKlafMOJhKkiRJmXEwVY3s2LEj6xJUMGZGqcyMUpkZpcpTZmxUq+j111/PugQVjJlRKjOjVGZGqfKUGRtVSZIk5ZKNahVNnDgx6xJUMGZGqcyMUpkZpcpTZvrdqIYQvhRCaAshxBBC843ev8Y2ZocQ1ocQ9pV/faz8/v0hhNbyrz0hhC+HEAbfaFnezJw5M+sSVDBmRv0VY6SlrYM9XUNpaevgvTwQVtXl9xmlylNmUs6ofhN4EDjSz/ffJoTQCPwR8EsxxhnAB4Hny4t3AgtijM3AbOADwGf7sSxXVq9enXUJKhgzo/441tnF8i9u4PGnt9B1eAePP72F5V/cwLHOrqxLUwH4fUap8pSZfjeqMcaNMcZ3zPV0rfev4nFgS4zxhfJ6PTHG18t/7ooxdpc/Nwi4DYg3WpY3nuFQKjOjG4kx8sQz2zhyqovunggRunsiR0518alntpkh3ZAZUao8ZaaW96jOBC6GEP6kfBn/2RDCmL6FIYTJIYSdwEngDPDf+rNMkt7LXjrSybGON+npffsPjp7eSHtHFy8dyc8zuSWp2gbWeF8rgMXAceDfAr8F/A2AGGMbMDeEMAT4feBjwNdvtOxyIYSngKf6Xg8dOpRVq1YBMHz4cBYvXsyWLVs4c+YMAHfddRdNTU0899xzlW0sWrSIrq4udu/eDUB9fT3Lli1j586dnDhxAoDx48cze/Zs1q5dy6VLlwCYM2cOU6dOrewPYNmyZRw+fJjDhw8DMGLECBYtWsTmzZs5d+4cANOmTWP8+PFs2LChst7999/P2bNn2bNnDwCDBw/mkUceobW1lddeew2ACRMmMGvWLNasWUNPTw8Azc3N1NfX09LSUtnWihUrOHjwIG1tbQCMGjWKBQsWsGnTJs6fPw/A3Xffzbhx49i4cWNlvSVLltDZ2Vl53m9DQwNLly5lx44dlWkrJk6cyMyZM1m9enXlf1/z5s1jwIABbN++vbKtlStXsn//ftrb2wEYPXo08+fP5/nnn6erq3Tpcvr06YwePZoXXnihst6DDz7IyZMn2b9/PwCNjY089NBDbN++nZMnTwLQ1NTE9OnT33aZYv78+fT29vLyyy/35YKVK1eyd+9ejh49CsCYMWO499572bBhAxcuXABgxowZjBw5ks2bN1e29fDDD3PixAkOHDgAwJAhQ3jggQdoaWmho6MDgMmTJzN16lTWrFlTWW/BggV0d3fT2toKQF1dHStWrGDPnj30PYRi7NixzJs3j/Xr13Px4kUAZs2axbBhw3jxxRcr21q6dCnHjx/nlVdeAUq5XrJkCVu3buX06dMATJkyhSlTprBu3brKegsXLuTChQvs2rULgIEDB7J8+XJ2797N8ePHARg3bhxz585l3bp1dHeXLlzMnj2bxsZGtm7dWtnWo48+Snt7O4cOHQJqczw1NDSwbdu2yrber8fTkVdP8kvNb9F3guN/HQr8PxN7WDwmEgIcPfCXLJj8sMfT2LE0Nzd7PF3leJo0aRKdnZ0eT/58Avp3PA0bNgygZsfT9SQ/mSqE0AZ8NMbY2p/3L1v+eWBOjPGJ8utZwKqrPYkghPDjwE/GGD+csuxKtX4y1cmTJxk9enTN9qfiMzO6kZa2Dh5/ekvpsj8wdVgvB8+WLobV1wW+9pnFLJg8KssSlXN+n1GqWmcmL0+m+t/AghDCsPLrv0ZpoBQhhKkhhPrynwcBPwrsutGyvLn8f2lSf5gZ3ch9k0YycVQjdQMCAJ+c2gtA3YBA06hG7ps0MsvyVAB+n1GqPGUmZXqqL4cQjgETgFUhhIPXe7+87LdDCB8BiDG2U7rcvzmEsAtYBvxs+aPLgJfL96G+DLwG/Eo/lknSe1oIgWc/vZBJdzRSXxcIoXQmdfIdjTz75CJCCFmXKEm3TL/vUY0x/kzK++Vlf/uK178H/N5VPvcV4CvX2MY1l0nS+8GEkY2sfWopLx3p5NXdW/jaZxZz36SRNqmS3vOS71Etklrfoxpj9AeHkpgZpTIzSmVmlKrWmcnLParveX2j/6T+MjNKZWaUyswoVZ4yY6NaRX3TW0j9ZWaUyswolZlRqjxlxkZVkiRJuWSjWkXOU6dUZkapzIxSmRmlylNmHEwlSZKkzDiYqkaef/75rEtQwZgZpTIzSmVmlCpPmbFRraK+5wJL/WVmlMrMKJWZUao8ZcZGVZIkSblko1pF06dPz7oEFYyZUSozo1RmRqnylBkb1SrK0yg5FYOZUSozo1RmRqnylBkb1Sp64YUXsi5BBWNmlMrMKJWZUao8ZcZGVZIkSblkoypJkqRccsL/KnrjjTe4/fbba7Y/FZ+ZUSozo1RmRqlqnRkn/K+RkydPZl2CCsbMKJWZUSozo1R5yoyNahXt378/6xJUMGZGqcyMUpkZpcpTZmxUJUmSlEs2qlXU2NiYdQkqGDOjVGZGqcyMUuUpMw6mkiRJUmYcTFUj27dvz7oEFYyZUSozo1RmRqnylBkb1SrK0yg5FYOZUSozo1RmRv0VY6SlrYOjx1+jpa2DPFx173ejGkL4UgihLYQQQwjNN3r/KuvfH0JoLf/aE0L4cghhcHnZgBDCF0MIe0MIu0IIz4UQpl62blMI4Y9DCN8pf+Zz7+7LlSRJ0pWOdXax/IsbePzpLZy90M3jT29h+Rc3cKyzK9O6Us6ofhN4EDjSz/evtBNYEGNsBmYDHwA+W172EeABYG6McQ6wFvi3ACGEAPxf4NkY4z0xxpnA/06ou2aampqyLkEFY2aUyswolZnRjcQYeeKZbRw51UV3T+TF7wW6eyJHTnXxqWe2ZXpmtd+NaoxxY4zxHSOTrvX+VT7XFWPsLr8cBNwG9H3lERgMNJQb02FA3zaXAxdjjN+4bFuv9bfuWpo+fXrWJahgzIxSmRmlMjO6kZeOdHKs4016ektt2Z8fLbWHPb2R9o4uXjrSmVltA2u5sxDCZOCPgLuAPwX+W3nRHwOPAieAc8CrwNLyspnA6yGErwP3AG3AP4wxfvcq238KeKrv9dChQ1m1ahUAw4cPZ/HixWzZsoUzZ84AcNddd9HU1MRzzz1X2caiRYvo6upi9+7dANTX17Ns2TJ27tzJiRMnABg/fjyzZ89m7dq1XLp0CYA5c+bQ2trKgAHf7/2XLVvG4cOHOXz4MAAjRoxg0aJFbN68mXPnzgEwbdo0xo8fz4YNGyrr3X///Zw9e5Y9e/YAMHjwYB555BFaW1t57bVSjz5hwgRmzZrFmjVr6OnpAaC5uZn6+npaWloq21qxYgUHDx6kra0NgFGjRrFgwQI2bdrE+fPnAbj77rsZN24cGzdurKy3ZMkSOjs72bdvHwANDQ0sXbqUHTt28PrrrwMwceJEZs6cyerVqyv/25o3bx4DBgx4243YK1euZP/+/bS3twMwevRo5s+fz/PPP09XV+mSwvTp0xk9ejQvvPBCZb0HH3yQkydPViYebmxs5KGHHmL79u2Ve66ampqYPn06q1evrqw3f/58ent7efnll/tywcqVK9m7dy9Hjx4FYMyYMdx7771s2LCBCxcuADBjxgxGjhzJ5s2bK9t6+OGHOXHiBAcOHABgyJAhPPDAA7S0tNDR0QHA5MmTmTp1KmvWrKmst2DBArq7u2ltbQWgrq6OFStWsGfPHvpmohg7diyvvfYagwcP5uLFiwDMmjWLYcOG8eKLL1a2tXTpUo4fP84rr7wClHK9ZMkStm7dyunTpwGYMmUKU6ZMYd26dZX1Fi5cyIULF9i1axcAAwcOZPny5ezevZvjx48DMG7cOObOncu6devo7i79P3L27Nk0NjaydevWyrYeffRR2tvbOXToEFCb46mhoYFt27ZVtuXxVBJjZNKkSR5PVzmempubWb9+vcfTFcdTT08P999/v8eTP5+Aqx9PnSPu4cNNPcwZWfr3um0g/ONtA/n87EsMGwSv7t7CnQPn3LLj6XqSp6cKIbQBH40xtvbn/WtsYwjw+8DXY4xfDyEspHSp/28AZ4F/D4yPMf6tcvP5r4DFMcY9IYSfBf52jPG+G+2n1tNTrVq1iscee6xm+1PxmRmlMjNKZWZ0Iy1tHTz+9Ba6e0o94b+69xL/ckfpXGZ9XeBrn1nMgsmjbtn+czc9VYzxPPB14CfLbz0BrIsxno4x9gK/S+kMK0A78HKMcU/59e8B94YQ6mtZsyRJ0nvRfZNGMnFUI3UDwtverxsQaBrVyH2TRmZUWQ0b1RDC1L7mMoQwCPhRoO+c73eBZeX3AT4E/GX5z/8fMCGE8APl138N2HfZ/a65MX/+/KxLUMGYGaUyM0plZnQjIQSe/fRCJt3RSH1d4H+31VNfF5h8RyPPPrmI0vChbPT7HtUQwpeBHwHGAatCCOdijFOv9X55nd8Gvh1j/DawDPj5EEJPeb9rgV8pb/43gRnAzhBCN6V7VX8WIMb4Rvly/5+WB1qdAX78Zr/wW6G3tzfrElQwZkapzIxSmRn1x4SRjax9aikvHenkyLG/YtKEO7lv0shMm1TwEapV5X1ASmVmlMrMKJWZUapaZyZ396hKkiRJN2KjWkVZnx5X8ZgZpTIzSmVmlCpPmfHSvyRJkjLjpf8a2bt3b9YlqGDMjFKZGaUyM0qVp8zYqFZR31MlpP4yM0plZpTKzChVnjJjoypJkqRcslGtojFjxmRdggrGzCiVmVEqM6NUecqMg6kkSZKUGQdT1ciGDRuyLkEFY2aUyswolZlRqjxlxka1ii5cuJB1CSoYM6NUZkapzIxS5SkzNqqSJEnKJRvVKpoxY0bWJahgzIxSmRmlMjNKlafM2KhW0ciRI7MuQQVjZpTKzCiVmVGqPGXGRrWKNm/enHUJKhgzo1Rm5u1ijLS0dfCNl47S0tbBe3kmm3fLzChVnjIzsD8fCiF8CfgIMAmYF2Nsvd77V1l/MvBVYB5wOMbYfNmyAcCvAj9crmcT8HdjjG+Vl/8T4FPAW8AF4OdjjNuSvkpJ0nvOsc4unnhmG0c7uqivG0B3Ty8TRzXy7KcXMmFkY9blSaqC/p5R/SbwIHCkn+9f6SzwS8DjV1n2JHBv+dcMoBf4BYAQQjPwWWBhubn9jfIvSdL7WIyRJ57ZxpFTXXT3RLre6qG7J3LkVBefemabZ1al94h+Naoxxo0xxnfMnH+t96/yuY4Y4wvAG1dZPBdYE2N8K5a+s/x/wCf7VgXqgdvLr0cAuZ3B/+GHH866BBWMmVEqM1Py0pFOjnW8SU/v2xvSnt5Ie0cXLx3pzKiy/DEzSpWnzPTr0v8tth34mRDCbwBvAj8GTAaIMe4MIfwX4HAIoQO4CFzzby+E8BTwVN/roUOHsmrVKgCGDx/O4sWL2bJlC2fOnAHgrrvuoqmpieeee66yjUWLFtHV1cXu3bsBqK+vZ9myZezcuZMTJ04AMH78eGbPns3atWu5dOkSAHPmzKGjo4PLn4S1bNkyDh8+zOHDhwEYMWIEixYtYvPmzZw7dw6AadOmMX78+LdNrnv//fdz9uxZ9uzZA8DgwYN55JFHaG1t5bXXXgNgwoQJzJo1izVr1tDT0wNAc3Mz9fX1tLS0VLa1YsUKDh48SFtbGwCjRo1iwYIFbNq0ifPnzwNw9913M27cODZu3FhZb8mSJXR2drJv3z4AGhoaWLp0KTt27OD1118HYOLEicycOZPVq1dXzl7MmzePAQMGsH379sq2Vq5cyf79+2lvbwdg9OjRzJ8/n+eff56uri4Apk+fzujRo3nhhRcq6z344IOcPHmS/fv3A9DY2MhDDz3E9u3bOXnyJABNTU1Mnz6d1atXV9abP38+vb29vPzyy325YOXKlezdu5ejR48CpcfD3XvvvWzYsKEyX9yMGTMYOXLk2+7Nefjhhzlx4gQHDhwAYMiQITzwwAO0tLTQ0dEBwOTJk5k6dSpr1qyprLdgwQK6u7tpbW0FoK6ujhUrVrBnz55KRsaOHcvw4cM5cuQIFy9eBGDWrFkMGzaMF198sbKtpUuXcvz4cV555RWglOslS5awdetWTp8+DcCUKVOYMmUK69atq6y3cOFCLly4wK5duwAYOHAgy5cvZ/fu3Rw/fhyAcePGMXfuXNatW0d3dzcAs2fPprGxka1bt1a29eijj9Le3s6hQ4eA2hxPDQ0NbNv2/bt8PJ6ofI0XL1583x9PnSPu4cNNPcwZWfr32ns68AffrePzsy8xbBC8unsLdw6c4/FE6XvNpEmTPJ78+QT07+dT39ezfv36mvx8up6kR6iGENqAj155L+q13r/K+o8Av3bFPaoB+JfAX6fUqK4Bfi7GOCqEMAX4GvDxGOPxEMLPAT8eY3ywP/XW+hGqq1at4rHHHqvZ/lR8ZkapzExJS1sHjz+9he6ed/4Mq68LfO0zi1kweVQGleWPmVGqWmcm149QjSVfiDHOizEuAfYCe8qLPw7sjjEeL7/+H8ADIYRBWdQqScqH+yaNZOKoRuoGhLe9Xzcg0DSqkfsm5Wd6HUnvXuaNagihIYQwsvzn0cA/pTQLAMB3KTWmQ8qvPwQc6JsRIG+GDBly4w9JlzEzSmVmSkIIPPvphUy6o5H6ukDjoDrq6wKT72jk2ScXUbpYJzAzSpenzPTr0n8I4cvAjwDjgFPAuRjj1Gu9X17nt4Fvxxi/HUJoBA4Ag4HhwPeA34sx/rMQwlhgPaXR/gOA/xpj/O/lbQTg3wI/Sun+1DeAz8UYv3+DyXXU+tK/JKm2Yoy8dKSTtpNvMHn07dw3aaRNqlQw17v0n3SPatHUulFtaWlhwYIFNdufis/MKJWZUSozo1S1zkyu71F9L+kbZSf1l5lRKjOjVGZGqfKUGRtVSZIk5ZKNahVNnjw56xJUMGZGqcyMUpkZpcpTZrxHtYp6enqoq6ur2f5UfGZGqcyMUpkZpap1ZrxHtUYuf/KD1B9mRqnMjFKZGaXKU2ZsVCVJkpRLNqqSJEnKJe9RraKOjg5GjfLZ0uo/M6NUZkapzIxS1Toz3qNaI93d3VmXoIIxM0plZpTKzChVnjJjo1pFra2tWZeggjEzSmVmlMrMKFWeMmOjKkmSpFyyUa0i56lTKjOjVGZGqcyMUuUpMw6mkiRJUmYcTFUje/bsyboEFYyZUSozo1RmRqnylBkb1Sry7K1SmRmlMjNKZWaUKk+ZsVGVJElSLtmoVtHYsWOzLkEFY2aUyswolZlRqjxlxsFUkiRJyoyDqWpk/fr1WZeggjEzSmVmlMrMKFWeMmOjWkUXL17MugQVjJlRKjOjVGZGqfKUGRtVSZIk5ZKNahXNmjUr6xJUMGZGqcyMUpkZpcpTZmxUq2jYsGFZl6CCMTNKZWaUyswoVZ4yY6NaRS+++GLWJahgzIxSmRmlMjNKlafM2KhKkiQpl2xUJUmSlEtO+F9FFy5coKGhoWb7U/GZGaUyM0plZpSq1plxwv8aOX78eNYlqGDMjFKZGaUyM0qVp8zYqFbRK6+8knUJKhgzo1RmRqnMjFLlKTM2qpIkScolG9UqGjp0aNYlqGDMjFKZGaUyM0qVp8w4mEqSJEmZcTBVjWzdujXrElQwZkapzIxSmRmlylNmbFSr6PTp01mXoIIxM0plZpTKzChVnjJjoypJkqRcslGtoilTpmRdggrGzCiVmVEqM6NUecqMg6mqqLu7m/r6+prtT8VnZpTKzCiVmVGqWmfGwVQ1sm7duqxLUMGYGaUyM0plZpQqT5mxUZUkSVIu2ahKkiQpl7xHtYo6OzsZOXJkzfan4jMzSmVmlMrMKFWtM+M9qjVy4cKFrEtQwZgZpTIzSmVmlCpPmbFRraJdu3ZlXULuxBhpaevgGy8dpaWtg/fyGfx3w8wolZlRKjOjVHnKzMD+fCiE8CXgI8AkYF6MsbX8/jTgd4HRwBngp2KMe66y/k8Dv3DZWxOAjTHGj4UQHgP+w2XLPgCciDHeW173U8DngR4gAr8YY/yzlC9S2TjW2cUTz2zjaEcX9XUD6O7pZeKoRp799EImjGzMujxJkpRz/T2j+k3gQeDIFe9/GfhKjPFuSs3mV6+2cozxf8QYm/t+ASeA/1letuqKZTv6loUQRgG/DvxQednnrrWPPBg4sF99//tCjJEnntnGkVNddPdEut7qobsncuRUF596ZptnVsvMjFKZGaUyM0qVp8wkDaYKIbQBH40xtoYQPgAcBEbFGC+FEALwV8CDMcaD19nGIuCPgR+IMXZfsWw8cAiYFGP8XghhNHAYuDfG+EoI4UPAL/edbb2RWg+m0ve1tHXwk09v5a2e3ncsq68LfO0zi1kweVQGlUmSpDy53mCqm2mZJwJ/FWO8BBBjjCGEdqCJUgN7LU8Cv3dlk1r2U8CfxRi/V97myRDCzwI7QggdwG3AimttOITwFPBU3+uhQ4eyatUqAIYPH87ixYvZsmULZ86cAeCuu+6iqamJ5557rrKNRYsW0dXVxe7duwGor69n2bJl7Ny5kxMnTgAwfvx4Zs+ezdq1a7l06RIAc+bM4dixY3R0dFS2tWzZMg4fPszhw4cBGDFiBIsWLWLz5s2cO3cOgGnTpjF+/Hg2bNhQWe/+++/n7Nmz7NlTuoti8ODBPPLII7S2tvLaa68BMGHCBGbNmsWaNWvo6ekBoLm5mfr6elpaWirbWrFiBQcPHqStrQ2AUaNGsWDBAjZt2sT58+cBuPvuuxk3bhwbN26srLdkyRI6OzvZt28fAA0NDSxdupQdO3bw+uuvAzBx4kRmzpzJ6tWrK2dI582bx4ABA3h19zZ+qbmbGOELO+r44Ym9LB5T+kzb+QG0nXyDC0f30NXVBcD06dMZPXo0L7zwQqWGBx98kJMnT7J//34AGhsbeeihh9i+fTsnT54EoKmpienTp7N69erKevPnz6e3t5eXX365LxesXLmSvXv3cvToUQDGjBnDvffey4YNGyo3jc+YMYORI0eyefPmyrYefvhhTpw4wYEDBwAYMmQIDzzwAC0tLZV/68mTJzN16lTWrFlTWW/BggV0d3fT2toKQF1dHStWrGDPnj30/edp7Nix1NXVcerUKS5evAjArFmzGDZsGC+++GJlW0uXLuX48eO88sorQCnXS5YsYevWrZw+fRooPe5uypQpb5ukeeHChVy4cKFyr9HAgQNZvnw5u3fv5vjx4wCMGzeOuXPnsm7dOrq7S4fk7NmzaWxsZOvWrZVtPfroo7S3t3Po0CGgNsdTQ0MD27Ztq2zr/Xw8bd++vbKtO++8k/r6etrb2wEYPXo08+fP5/nnn3/fH0/Nzc2sX7/e4+mK42n48OHcc889Hk9XOZ5WrlzJ/v37PZ6uOJ76slWr4+l6buaM6nzgazHGey5bvg34pzHGqz7SIIRwO6WzrotjjHuvWBaAV4Cf77sHNYQwHPgL4FMxxn0hhA8DvwbMiDG+daN6a31GddWqVTz22GM121+etbR18PjTW+jueWe+PKP6fWZGqcyMUpkZpap1Zm7V9FRHgTtDCAPLOwmUzqa2X2edvwnsubJJLVsKNACrLnvvh4DTMcZ9ADHGPwaGURrUpRy7b9JIJo5qpG5AeNv7dQMCTaMauW+Sc/pJkqTre9eNavny/A7gb5Xf+jhw7Hr3p1K67P8711n21Rhjz2XvfRdoDiGMAwgh3E/pdoWj77buW2ncuHFZl5AbIQSe/fRCJt3RSH1doHFQHfV1gcl3NPLsk4so/b9GZkapzIxSmRmlylNm+nXpP4TwZeBHgHHAKeBcjHFqCOEeSqPw7wDOAj8dY9xdXue3gW/HGL9dfn0P8BIwPsZ47ortDweOA7NjjN+9YtkvAD8DdAOXKN1a8Bf9+eIcTJW9GCMvHemk7eQbTB59O/dNGmmTKkmSKq536d9HqFbRunXrWLZsWc32p+IzM0plZpTKzChVrTPjI1RrpG+Up9RfZkapzIxSmRmlylNmbFQlSZKUSzaqVTR79uysS1DBmBmlMjNKZWaUKk+ZsVGtosZGn1+vNGZGqcyMUpkZpcpTZmxUq+jyJ49I/WFmlMrMKJWZUao8ZcZGVZIkSblkoypJkqRcch7VKnrrrbcYNGhQzfan4jMzSmVmlMrMKFWtM+M8qjXS3t6edQkqGDOjVGZGqcyMUuUpMzaqVXTo0KGsS1DBmBmlMjNKZWaUKk+ZsVGVJElSLtmoVtHw4cOzLkEFY2aUyswolZlRqjxlxsFUkiRJyoyDqWpky5YtWZeggjEzSmVmlMrMKFWeMmOjWkVnzpzJugQVjJlRKjOjVGZGqfKUGRtVSZIk5dJ7+h7VEMJF4PUa7nIIcL6G+1PxmRmlMjNKZWaUqtaZGRNjHHy1Be/pRrXWQgjHrnUzsHQ1ZkapzIxSmRmlylNmvPQvSZKkXLJRlSRJUi7ZqFbXF7MuQIVjZpTKzCiVmVGq3GTGe1QlSZKUS55RlSRJUi7ZqEqSJCmXbFQlSZKUSzaqCUIIj4QQWt/lul8KIbSFEGIIobm6lSmvbjIzq0MIu0IIrSGE50MI86pcnnLoJjPTFkL4TjkzrSGET1S5POXQu81MCOGOy7LSGkI4EEK4FEIYdQvKVI7c5PeZHw4hvFT++bQlhDC3yuW9zcBbuXG9zTeBXwVeyLoQFcaPxRhPA4QQfhT4KnBLvyHoPeETMcbWrItQ/sUYTwHNfa9DCJ8HlsYYOzIrSrkWQhgJ/E/g4RjjnhDCQ+XXH7xV+/SM6jWEEG4LIfxBCGFvCGFnCGF1edHAEMJ/K7+3J4RwX/nzk0MIpy9bf0gIoTKlQoxxY4zxWG2/CtXSLcjM6cs2Pxxwio73mGpnRu99tzgzTwK/c2u/AtValTNzF3AqxrgHIMb4PNAUQrj3VtVvo3ptPwyMiDHOjDHOBX68/P504HfL7/068G+yKlC5U/XMhBCeDSEcBX4F+GS1C1bmbsX3mWdDCLtDCL8TQhhT5XqVvVvysymEsAQYCfxJNYtVLlQzM68Ad5TzQgjhI8BQYHLVqy6zUb22ncCM8v82PgF0l98/GGPcWv7zi5T+dyHBLchMjPGJGONE4JeA/1DVapUH1c7MwzHGOcC9wEngd6tarfLgVv1sehJ4NsZ4qUp1Kj+qlpkY4xngbwD/LoSwHVgJ7AVuWW5sVK8hxvhdYCbw58ADwF9S+t/mhcs+1sP37/O9BNRdtqyhBmUqR25lZmKMvws8GkK4o5o1K1vVzkyMsb38ezfwa8BDt6JuZedWfJ8JIQwBfgx45haUrIzdgu8zz8UYl8YY5wP/EBhPqVm9JWxUryGEMAGIMcZvA58HAjDxOqucKK0WZpZfP3GLS1TOVDMzIYQRIYTxl73+KHAKcJDDe0iVM3N7CGHEZZ/9CeDl6lasrN2in02fAHbGGPdXtVjlQrUzE0K487KX/y+wLsZ4sIolv42j/q9tNqVT24HS39PvAbuu9eEY46UQwueAPwkhnKI0yr8ihPBl4EeAccCqEMK5GOPUW1a9slDNzAwHvhFCuA3oBV4HPhR95vF7TTUzMxb4wxBCHaUfRN/F/zC/F1X1Z1PZk8DTt6JY5UK1M/PLoTTafyClWwaevDVllwR/7kmSJCmPvPQvSZKkXLJRlSRJUi7ZqEqSJCmXbFQlSZKUSzaqkiRJyiUbVUmSJOWSjaokSZJyyUZVkiRJufT/A6VmXCjmInAqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#0数据准备\n",
    "x=[i for i in range(1,10)]\n",
    "y=[random.uniform(10,15) for i in x]\n",
    "\n",
    "# 1创建画布\n",
    "plt.figure(figsize=(10,8),dpi=80)\n",
    "\n",
    "\n",
    "# 2绘制图像\n",
    "plt.scatter(x,y,label=\"shuju\")\n",
    "\n",
    "# 3添加辅助层\n",
    "# x,y刻度显示内容+刻度单位构造\n",
    "x_ticks_label=[\"shu{}\".format(i) for i in x]#构建刻度标\n",
    "y_ticks_label=range(10,15)\n",
    "\n",
    "plt.xticks(x[::2],x_ticks_label[::2])\n",
    "plt.yticks(y[::1])\n",
    "\n",
    "# X,Y轴描述\n",
    "plt.xlabel=(\"x_data\")\n",
    "plt.ylabel=(\"y_data\")\n",
    "\n",
    "plt.grid(True,linestyle=\"--\")\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"SHUJUCESHI\",fontsize=20,color=\"r\")\n",
    "\n",
    "\n",
    "# 4展示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a6a3b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 柱状图或条形图 统计离散型数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ccc9d516",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_11186/2874873719.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# 2绘制图像\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"shuju\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# 3添加辅助层\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2651\u001b[0m     return gca().bar(\n\u001b[1;32m   2652\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2653\u001b[0;31m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1359\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2304\u001b[0m         x, height, width, y, linewidth, hatch = np.broadcast_arrays(\n\u001b[1;32m   2305\u001b[0m             \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2306\u001b[0;31m             np.atleast_1d(x), height, width, y, linewidth, hatch)\n\u001b[0m\u001b[1;32m   2307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2308\u001b[0m         \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(subok, *args)\u001b[0m\n\u001b[1;32m    536\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m     \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    421\u001b[0m     \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
     ]
    },
    {
     "data": {
      "image/png": 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BABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGCE8AQAYITwBABghPAEAGLFyeFbV5VX1UFU9XlUPV9WVL7Lud6vq36rqO1V1Z1W9bn3jAgCwqXZyxvP2JHd09xVJbkly9+kLquotSf4iydVJLkvyM0k+fO5jAgCw6VYKz6q6KMlVSe5ZHro3ycGquuy0pdcnua+7v9fdneS2JB9a17AAAGyufSuuO5jk6e5+Pkm6u6vqWJJLkhw9Zd0lSb57yvMnl8f+n6o6nOTwKYdeqKqnV5yHzbc/yYndHoIx9ntvsd97i/3eWw6cy4tXDc+16+4jSY6cfF5V2929tVvzMMt+7y32e2+x33uL/d5bqmr7XF6/6mc8n0pycVXtW/5DK4szmcdOW3csyZtPeX7pGdYAALAHrRSe3f1MkkeS3LA8dF2S7e4+etrSe5NcW1UHlnF6c5JPrWtYAAA2106uaj+U5FBVPZ7kI0luSpKququqrk2S7n4iyUeTfDmLz34+m8XV8Ks48vJLeBWx33uL/d5b7PfeYr/3lnPa71pcfA4AAOeXOxcBADBCeAIAMEJ4AgAwYiw83et9b1llv6vqmqr6alV9u6q+VVW3VpU/DG2gVb+/l2urqu6vqu8Pjsga7eDn+c9X1Zeq6rHl4wPTs3LuVvx5/pqqOrL8ef4vVfXAGe5uyAaoqr+pqierqqvql15i3Vn12uT/5N3rfW952f1O8h9JPtjdb0vyK0nemeTGsQlZp1X2+6Q/TPKdiaE4b1b5ef4TST6b5E+7+61J3p7knyeHZG1W+f6+Nsm7kvxid/9Cki8m+djYhKzTZ5K8Oz96J8ofcS69NhKe7vW+t6y639399eWv4Ep3/1eSR7O46QAbZAff31meKfmtJH89NiBrtYP9/u0kX+nuB5Oku3/Y3c/OTco67GC/O8kFSX58+Xu8L0xyTne4YXd09z9198vt3Vn32tQZz/93r/cs7mh0+n3cV77XO69oq+73/6mqA1n8h/z5kQlZp5X2e/k2zJ1Z/E7gH04Pydqs+v39tiT/XVWfr6pHq+qTVfXG4Vk5d6vu9+eSfCnJ95I8neTXk/zZ3JgMO+te83k6dl1VXZjFD61bu/truz0P581Hk/xDdz+224MwYl+S38jiDxq/nOR4ko/v6kScT1dl8XGKNyX52Szear9tVyfiFWkqPN3rfW9Zdb9TVa9P8oUkn+1ud7/YTKvu93uS/H5VPZnkwSQXLj/A7izYZtnJz/MHuvv48izZPUl+bXRS1mHV/b4xyf3d/f3ufiHJJ5K8d3RSJp11r42Ep3u97y2r7ndV7c8iOr/Q3X85OyXrsup+d/fV3f3m7r40iw+uP9fdl/rc32bZwc/zTyd5x/IdjSR5X5JvzEzJuuxgv59Ick1V/djy+fuTfHNmSnbBWffa2C0zq+rnsrgS7qeTPJfkpu7+16q6K4sPqN63XPd7WdwLPll8XuTm7v7ByJCszSr7XVV/kuTPk3zrlJf+fXf/1fS8nJtVv79PWX9pkke7+yeHR2UNdvDz/HeS/FGSF7J4q/3D3f3U7kzN2Vrx5/kFSf42iz9U/iCLz3refPICUjZHVd2e5DeTHEjy70n+s7svW1evuVc7AAAjXFwEAMAI4QkAwAjhCQDACOEJAMAI4QkAwAjhCQDACOEJAMAI4QkAwIj/BSF9JJyxBeiYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#0数据准备\n",
    "x=[i for i in range(1,10)]\n",
    "label_y = [i for i in range(10,20)]\n",
    "\n",
    "# 1创建画布\n",
    "plt.figure(figsize=(10,8),dpi=80)\n",
    "\n",
    "\n",
    "# 2绘制图像\n",
    "plt.bar(x, weights=1,height=label_y,label=\"shuju\")\n",
    "\n",
    "# 3添加辅助层\n",
    "# x,y刻度显示内容+刻度单位构造\n",
    "x_ticks_label=[\"shu{}\".format(i) for i in x]#构建刻度标\n",
    "y_ticks_label=range(10,15)\n",
    "\n",
    "plt.xticks(x[::2],x_ticks_label[::2])\n",
    "plt.yticks(y[::1])\n",
    "\n",
    "# X,Y轴描述\n",
    "plt.xlabel=(\"x_data\")\n",
    "plt.ylabel=(\"y_data\")\n",
    "\n",
    "plt.grid(True,linestyle=\"--\")\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"SHUJUCESHI\",fontsize=20,color=\"r\")\n",
    "\n",
    "\n",
    "# 4展示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02324c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae6fe80",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#0数据准备\n",
    "x=\n",
    "\n",
    "# 1创建画布\n",
    "\n",
    "# 2绘制图像\n",
    "\n",
    "# 3添加辅助层\n",
    "\n",
    "# 4展示图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f82a6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb0aca89",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#0数据准备\n",
    "x=\n",
    "\n",
    "# 1创建画布\n",
    "\n",
    "# 2绘制图像\n",
    "\n",
    "# 3添加辅助层\n",
    "\n",
    "# 4展示图像"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
